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Server Quality Checklist

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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a distinct market data aspect: orderbook depth, whale flow, and market pulse. There is no functional overlap, and an agent can easily select the right tool based on the specific intelligence needed.

    Naming Consistency5/5

    All tool names follow a consistent 'get_' prefix followed by a descriptive noun phrase (orderbook_depth, whale_flow, market_pulse). The naming pattern is uniform and predictable.

    Tool Count5/5

    With only 3 tools, the server is tightly scoped to its purpose of providing DEX market intelligence. Each tool covers a distinct data category, and the count is well-suited for a focused, specialized server.

    Completeness4/5

    The tool surface covers the core market intel needs: orderbook, whale activity, and market momentum. Potential minor gaps like historical data or token metadata exist, but for the stated purpose, the coverage is nearly complete without dead ends.

  • Average 3.6/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 22 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing
  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden of disclosing behavioral traits. It mentions that the tool fetches data (implying read-only) and includes cost information, but it does not disclose authentication needs, rate limits, or potential side effects. Given the lack of annotations, this is insufficient.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness4/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single concise sentence, front-loaded with cost and then the action. It avoids redundancy and is easy to parse, though it could have included more detail on the parameter without becoming verbose.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool has only one parameter and an output schema, the lack of return value explanation is acceptable. However, the absence of usage guidance and parameter semantics makes the description incomplete for a simple tool, limiting its overall usefulness.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, so the description must explain the parameter. However, it does not elaborate on what 'token' means (e.g., address, symbol, format). The only clue is the name, which is not sufficient for unambiguous use.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the verb 'Fetches' and the specific resource: 1h smart money accumulation, net whale inflow (USD), and cluster accumulation confidence signals. It differentiates from sibling tools which focus on orderbook depth and market pulse, making its distinct purpose evident.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no explicit guidance on when to use this tool versus alternatives. The usage is implied by the tool's name and description—for whale flow analysis—but there are no explicit conditions, exclusions, or mentions of preferable scenarios.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations present, the description carries the full burden. It discloses that data is real-time, covers a 5m window, includes several specific metrics, and notes a $0.02 USDC cost. However, it does not disclose whether the tool is safe/read-only, what may cause failures, or any rate limits or token formatting requirements.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, front-loaded sentence that conveys useful information efficiently: cost, data freshness, returned metrics, and acceptable token types. There is no fluff or repetition of the schema properties.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a one-parameter tool with an output schema, the description is largely complete: it specifies the input domain, the key metrics returned, and the Tier 1 cost. It could be more complete by mentioning any preconditions such as token format or chain context, but given low complexity, it covers most necessary context.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, so the description must compensate. It adds that 'token' can be any cryptocurrency or DEX token, which gives the parameter broad scope. It does not clarify whether the token should be a symbol, address, or standardized identifier, but the schema contains only one required parameter and the description supplies the essential semantic.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description has a specific verb ('Fetches') and states a clear deliverable: real-time 5m momentum, buy/sell counts, volume, and buy-pressure score. It also distinguishes from sibling tools like get_orderbook_depth and get_whale_flow by focusing on token momentum/sentiment rather than order books or whale activity.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No explicit guidance is given about when to use this tool versus the sibling alternatives. The mention of 'Tier 1 ($0.02 USDC)' provides pricing information but not use-case direction. 'Any cryptocurrency or DEX token' defines token scope but not the situational choice between this and get_orderbook_depth or get_whale_flow.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description carries the transparency burden and does well by disclosing that the data is 'simulated' and that slippage is estimated for $10k trades. It also surfaces the pricing tier, which is useful behavioral context. It could add more about output shape or data freshness, but the output schema covers some of that.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    A single, front-loaded sentence with no filler. It packs pricing, data type, and key metrics efficiently while remaining readable.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a one-parameter tool with an output schema, the description covers purpose, cost, simulation nature, and slippage context. It is slightly incomplete in that it omits token parameter semantics and any comparison to sibling tools, but the overall picture is sufficient for basic selection and invocation.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, and the description does not mention the 'token' parameter at all. The token's meaning is somewhat inferable from the tool name and context, but the description fails to explicitly connect the parameter to token symbol, address, or accepted format, leaving a gap.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description uses a specific verb ('Fetches') with a concrete resource ('simulated orderbook bid/ask depth') and adds differentiating details: liquidity imbalance, estimated slippage in bps, and $10k trade size. This clearly distinguishes it from sibling tools like get_whale_flow and get_market_pulse.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines3/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies usage for orderbook depth and slippage analysis, and the 'Tier 2 ($0.05 USDC)' cost hints at when it applies, but it provides no explicit guidance on when to prefer this tool over the sibling tools. No alternatives or exclusions are stated.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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